FigureYa259circLink
Author(s): Zongcheng Li
Reviewer(s): Ying Ge, Yasi Zhang
Date: 2025-11-21
1 Academic Citation
If you use this code in your work or research, we kindly request that you cite our publication:
Xiaofan Lu, et al. (2025). FigureYa: A Standardized Visualization Framework for Enhancing Biomedical Data Interpretation and Research Efficiency. iMetaMed. https://doi.org/10.1002/imm3.70005
2 需求描述 Requirement description
画出这种连线图。
Draw this connection diagram.
From https://molecular-cancer.biomedcentral.com/articles/10.1186/s12943-021-01322-w, from the same article as FigureYa260CNV.
Fig. 5 Transcriptional and post-transcriptional characteristics associated with the WM_Score. a Differences in miRNA-targeted signaling pathways in the TCGA-COAD/READ cohort between the WM_Score-high and -low groups. The red line represents a low expression of miRNA in the high WM_Score group, and the blue line represents a high expression of miRNA in the low WM_Score group. Red dots correspond to miRNA-targeted genes highly expressed in the high WM_Score group, and blue dots correspond to miRNA-targeted genes highly expressed in the low WM_Score group. The circle represents a signaling pathway enriched with targeted genes.
类似的图：
A similar image:
出自：https://doi.org/10.1038/s42255-019-0045-8，跟FigureYa174squareCross、FigureYa199crosslink、FigureYa256panelLink出自同一篇文章。这篇文章以连线著称，总是被模仿，不知道会不会被超越。
From https://doi.org/10.1038/s42255-019-0045-8, from the same article as FigureYa174squareCross, FigureYa199crosslink, and FigureYa256panelLink. This paper is renowned for its connection designs—constantly imitated, yet to be surpassed.
Fig. 3 | overview of the propensity score algorithm and the hypoxia-associated molecular patterns across cancer types. c, Association between mRNA expression levels of hypoxia-associated genes and drug sensitivity across 1,074 cancer cell lines by Spearman’s rank correlation. The dark green dots along the x axis indicate hypoxia-related genes; the orange dots denote drugs that are clustered by different signalling pathways. The size of the orange dot indicates the number of genes correlated with drug sensitivity (|rs| > 0.3, FDR < 0.05); the bar plot shows the number of drugs correlated with the genes. The pink and cyan lines indicate positive and negative correlation, respectively. JNK, Jun N-terminal kinase.
3 应用场景 Application scenarios
展示miRNA-靶基因（或基因-药物等）的关系，连线和节点的颜色代表节点类型（例如例文的high和low WM_Score）。同一通路的基因画在同一圆圈里，并标注通路名。
为了画这个图，完善了crosslink包，该R包会继续添加更多有趣的连线功能，感兴趣可前往https://github.com/zzwch/crosslink查看最新版本及功能，在github上还能提交issue跟作者直接交流。
This figure displays miRNA-target gene (or gene-drug, etc.) relationships, where the colors of connecting lines and nodes represent node types (e.g., high vs. low WM_Score as shown in the example). Genes from the same pathway are grouped within circular clusters labeled with pathway names.
To create this visualization, we enhanced the crosslink R package, which will continue to incorporate more innovative connection features. Those interested can visit https://github.com/zzwch/crosslink to explore the latest version and functionalities. GitHub also allows users to submit issues for direct communication with the author.
4 环境设置 Environment Setup
## Starting R package installation...
## ===========================================
##
## Installing CRAN packages...
## Package already installed: ggplot2
## Package already installed: magrittr
## Package already installed: tidyverse
## Package already installed: rlang
## Package already installed: patchwork
##
## ===========================================
## Package installation completed!
## You can now run your R scripts in this directory.source("crosslink.R") # From R package crosslink
source("layout.R") # From R package crosslink
source("transfromation.R") # From R package crosslink
source("utils.R") # From R package crosslink
library(magrittr) # 管道操作符 Pipe operations
library(tidyverse) # 数据整理与可视化 Data wrangling & visualization
library(ggplot2) # 绘图 Plotting
library(rlang) # 编程辅助 Programming tools
library(patchwork) # 拼图 Plot composition
# 显示英文报错信息
# Show English error messages
Sys.setenv(LANGUAGE = "en")
# 禁止chr转成factor
# Prevent character-to-factor conversion
options(stringsAsFactors = FALSE) 5 输入文件 Input Files
easy_input_links.csv，连线表示source（miRNA）和target（靶基因）的关系。连线的颜色表示source的类型source_type（high WM_Score和low WM_Score）。
easy_input_nodes.csv，key（包括source和target）所在的path（通路）信息。
- 节点的颜色表示target的类型type（high WM_Score和low WM_Score）。
- source（miRNA）的path列写为source，source的type列是source的type，因此跟easy_input_links.csv的source_type列一致，signif列的内容会标在图中source的名字下面。
- 如果想要例文2那样的效果，用target节点的大小表示number of genes
correlated to drug
sensitivity，就加一列size，用于控制targe节点的大小，记得把文档中
# size = size前面的#删掉。
easy_input_links.csv represents the relationship between the source (miRNA) and the target (target gene), where the color of the line indicates the type of the source (source_type: high WM_Score and low WM_Score).
easy_input_nodes.csv contains the pathway information (path) for the keys (including both source and target).
- The color of the nodes represents the type of the target (type: high WM_Score and low WM_Score).
- For the source (miRNA), the path column should be labeled as “source,” and the type column corresponds to the source’s type, which should match the source_type column in easy_input_links.csv. The content of the signif column will be displayed below the name of the source in the diagram.
- If you want an effect similar to Example 2, where the size of the
target nodes represents the “number of genes correlated to drug
sensitivity,” add a column named “size” to control the size of the
target nodes. Remember to remove
#in# size = sizein the document.
links <- read.csv("easy_input_links.csv")
nodes <- read.csv("easy_input_nodes.csv")
# 获取所有通路的名字
# Get all pathway names
paths <- unique(nodes[nodes$path != "source", ]$path)
# 把"source"排在前面
# Place "source" first
nodes$path <- factor(nodes$path, levels = c("source", paths))
# 连线的颜色
# Line colors
src_up_col <- "red"
src_dn_col <- "blue"
# target节点的颜色
# Target node colors
tar_up_col <- "red"
tar_dn_col <- "blue"6 开始画图 Plotting
6.1 快速预览 Take a glance of the data
重要提示！节点和边数据中不能包含列名 ‘node’、‘cross’、‘node.type’、‘x’、‘y’、‘degree’！
IMPORTANT! The colnames of ‘node’, ‘cross’, ‘node.type’, ‘x’, ‘y’, ‘degree’ MUST NOT BE included in nodes and edges!
# 使用crosslink函数创建网络布局对象
# Create network layout object using crosslink function
toy <- crosslink(
nodes = nodes,
edges = links,
cross.by = "path",
xrange = c(0, 10),
yrange = c(-5, 5),
spaces = "partition")
# 绘制网络布局图
# Plot the network layout
cl_plot(toy)
6.2 按通路将靶点转换为圆形分布 Transform the targets into circle by pathways
# 自定义函数
# Custom function
toCircle <- function(x, y, rx = 1, ry =1, intensity = 2){
mapTo2pi <- function(x) {scales::rescale(c(0, x), to = c(0, 2*pi))[-1]}
data.frame(x, y) %>%
mutate(group = paste0("group", x)) %>%
mutate(yy = scales::rescale(-x, to = range(y))) %>%
mutate(xx = mean(x) + intensity * sin(yy %>% mapTo2pi),) %>%
group_by(group) %>%
mutate(tri = rank(y, ties.method = "first") %>% mapTo2pi) %>%
ungroup() %$%
data.frame(
x = xx + rx*sin(tri),
y = yy + ry*cos(tri))
}
# 应用圆形变换函数到网络布局对象
# Apply circular transformation to network layout object
toy_circle <- toy %>% tf_fun(
crosses = paths,
along = "xy",
fun = toCircle,
rx = 0.2, ry = 0.2)
# 绘制变换后的网络图（不显示标签）
# Plot transformed network (without labels)
toy_circle %>% cl_plot(label = NA)
6.3 后处理 Post-processing
## 对圆形布局进行几何变换
## Geometric transformations for circular layout
toy_final <- toy_circle %>%
tf_rotate(angle = -90) %>%
tf_flip(axis = "x", crosses = paths) %>%
tf_shift(y = 8, crosses = paths, relative = F) %>%
set_header()
## 可视化后处理结果
## Visualize post-processing results
toy_final %>% cl_plot(label = NA) %>% cl_void()
6.4 微调 Fine tuning
## Available meta.data names are showing below.
## Cross: node, node.type, x, y, cross, key, type, path, signif, degree
## Link: src, tar, source_type, src.cross, tar.cross, source, target, src.degree, tar.degree, x, y, xend, yend
## Header: node, node.type, x, y, cross, headerggplot() +
# 每个模块相对独立，可根据需要调整不同图层的叠加顺序
# Each module is relatively independent, you can adjust the layer stacking order as needed
# 路径的黑色圆圈（绘制在最底层，部分会被靶点覆盖）
# Black circles for pathways (drawn at the bottom layer, partially covered by target points)
ggforce::geom_circle(
mapping = aes(x0 = x0, y0 = y0, r = r),
data = get_cross(toy_final) %>% filter(cross != "source") %>%
group_by(path) %>%
transmute(
x0 = mean(x),
y0 = mean(y),
r = 0.2
) %>% unique(),
show.legend = F
) +
# 连线（miRNA-靶基因关系）
# Connection lines (miRNA-target relationships)
geom_segment(
mapping = aes(x, y, xend = xend, yend = yend, color = source_type),
data = get_link(toy_final),
alpha = 0.3
) +
# 靶点节点
# Target nodes
geom_point(
mapping = aes(x, y,
# size = size,
color = type),
data = get_cross(toy_final) %>% filter(cross != "source")
) +
# 添加文字：靶点所属通路名称
# Add text: Pathway names for targets
ggrepel::geom_text_repel(
mapping = aes(x, y, label = header), nudge_y = 0.3,
data = get_header(toy_final) %>% filter(cross != "source"),
segment.color = NA
) +
# 添加文字：miRNA源节点名称
# Add text: miRNA source node names
geom_text(
mapping = aes(x, y, label = key), angle = 90, hjust = 1, nudge_y = -0.1,
data = get_cross(toy_final) %>% filter(cross == "source")
) +
# 添加文字：每个通路的靶点数量
# Add text: Number of targets per pathway
geom_text(
mapping = aes(x, y, label = num),
data = get_cross(toy_final) %>% filter(cross != "source") %>%
group_by(path) %>%
transmute(
x = mean(x),
y = mean(y),
num = n()
) %>% unique()
) +
# 颜色配置
# Color settings
scale_color_manual(values = c(
src_up = src_up_col, src_dn = src_dn_col,
tar_up = tar_up_col, tar_dn = tar_dn_col)) +
labs(x = NULL, y = "Target_Pathway") +
scale_y_continuous(expand = expansion(mult = c(0.25,0.1))) -> p
p
如果想要像例文2那样给source也画上点，就运行下面这段
If you want to plot points for the source nodes as in Example 2, run the following code
6.5 添加注释图 Add annotation plots
把source的’signif’标注在source名字的下方
Place the ‘signif’ annotation below the source names
# 创建带注释的circLink图
# Create circLink plot with annotations
cl_plot2(
p %>% cl_void(th = theme(
axis.title = element_text())),
object = toy_final,
annotation = cl_annotation(
bottom = ggplot() +
geom_text(
mapping = aes(seq_along(key), 0, label = signif),
data = nodes %>% filter(path == "source")
) + theme_void()
,
bottom.by = "source", bottom.height = 0.05
)
)
7 附：示例数据生成过程 Appendix: Example Data Generation Process
# 生成节点名称
# Generate node names
sources <- paste0("source", 1:20 %>% format)
targets <- paste0("target", 1:500 %>% format)
paths <- paste0("path", 1:15 %>% format)
# 创建节点数据框
# Create node dataframe
nodes <- data.frame(
key = c(sources, targets),
type = c(rep("src_up", length(sources)/2),
rep("src_dn", length(sources)/2),
sample(c("tar_up", "tar_dn"), length(targets), replace = T)),
path = c(rep("source", length(sources)),
rep(paths, times = c(
40, 50, 30, 30, 50, 50, 20, 30, 30, 40, 20, 30, 30, 20, 30
))) %>% factor(
levels = c("source", paths)
),
signif = c(sample(c("*", "**", "***", "ns"), length(sources), replace = T),
rep(NA, length(targets)))
)
# 生成连接关系数据
# Generate link relationships
link_n <- 500
set.seed(666)
links <- data.frame(
src = sample(sources, link_n, replace = T),
tar = sample(targets, link_n, replace = T)) %>%
unique() %>%
mutate(source_type = nodes$type[match(src, nodes$key)])
# 保存示例数据文件
# Save example data files
write.csv(links, "easy_input_links.csv", row.names = F, quote = F)
write.csv(nodes, "easy_input_nodes.csv", row.names = F, quote = F)8 Session Info
## R version 4.5.1 (2025-06-13)
## Platform: x86_64-apple-darwin20
## Running under: macOS Monterey 12.7.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Asia/Shanghai
## tzcode source: internal
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] crosslink_0.1.0 patchwork_1.3.2
## [3] rlang_1.1.6 lubridate_1.9.4
## [5] forcats_1.0.1 stringr_1.5.2
## [7] purrr_1.2.0 readr_2.1.5
## [9] tibble_3.3.0 tidyverse_2.0.0
## [11] ggstatsplot_0.13.3 tidyr_1.3.1
## [13] dplyr_1.1.4 data.table_1.17.8
## [15] shape_1.4.6.1 future_1.67.0
## [17] survival_3.8-3 TCGAbiolinks_2.36.0
## [19] SeuratData_0.2.2.9002 RColorBrewer_1.1-3
## [21] ggrepel_0.9.6 ggraph_2.2.2
## [23] Seurat_5.3.1 SeuratObject_5.2.0
## [25] sp_2.2-0 SingleCellExperiment_1.30.1
## [27] SummarizedExperiment_1.38.1 GenomicRanges_1.60.0
## [29] GenomeInfoDb_1.44.3 MatrixGenerics_1.20.0
## [31] matrixStats_1.5.0 plyr_1.8.9
## [33] corrplot_0.95 magrittr_2.0.4
## [35] igraph_2.2.0 reshape2_1.4.4
## [37] ggplot2_4.0.1 clusterProfiler_4.16.0
## [39] org.Hs.eg.db_3.21.0 AnnotationDbi_1.70.0
## [41] IRanges_2.42.0 S4Vectors_0.46.0
## [43] Biobase_2.68.0 BiocGenerics_0.54.1
## [45] generics_0.1.4
##
## loaded via a namespace (and not attached):
## [1] R.methodsS3_1.8.2 dichromat_2.0-0.1
## [3] progress_1.2.3 goftest_1.2-3
## [5] Biostrings_2.76.0 vctrs_0.6.5
## [7] ggtangle_0.0.7 spatstat.random_3.4-2
## [9] effectsize_1.0.1 digest_0.6.39
## [11] png_0.1-8 bayestestR_0.17.0
## [13] correlation_0.8.8 deldir_2.0-4
## [15] parallelly_1.45.1 MASS_7.3-65
## [17] httpuv_1.6.16 qvalue_2.40.0
## [19] withr_3.0.2 xfun_0.54
## [21] ggfun_0.2.0 ellipsis_0.3.2
## [23] memoise_2.0.1 MatrixModels_0.5-4
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## [33] prettyunits_1.2.0 datawizard_1.3.0
## [35] rematch2_2.1.2 KEGGREST_1.48.1
## [37] promises_1.3.3 httr_1.4.7
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